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Extended dissipativity of semi-Markov jump neural networks with partly unknown transition rates

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Abstract In this paper, the synchronization problem is addressed for semi-Markov jump master-slave neural networks (SMJNN) with extended ( X , Y , Z ) -dissip-ativity performance and partly unknown… Click to show full abstract

Abstract In this paper, the synchronization problem is addressed for semi-Markov jump master-slave neural networks (SMJNN) with extended ( X , Y , Z ) -dissip-ativity performance and partly unknown transition rates (TR). Comparing with constant TR in Markovian jump neural networks, the TR of SMJNN depend on the sojourn time (ST) of semi-Markov process. First, some sufficient stability and extended ( X , Y , Z ) -dissipativity criteria are established for the error system with partly unknown TR. Then, ST-dependent dissipativity criterion is transformed to feasible condition by the upper and lower bounds of TR. Furthermore, the state feedback controller is also designed to ensure the synchronization of SMJNN. Finally, a numerical example is provided to verify the admissibility and effectiveness of the acquired results.

Keywords: dissipativity; jump; neural networks; partly unknown; semi markov

Journal Title: Neurocomputing
Year Published: 2021

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